Applied Statistics - Anna Mikusheva 

 This week, the Applied Statistics Workshop will present a talk by  Anna Mikusheva , a Ph.D. candidate in the Economics Department at Harvard.  Before joining the graduate program at Harvard, she received a Ph.D. in mathematics from Moscow State University.  She will present a talk entitled "Uniform inferences in autoregressive processes."  The paper is available from the workshop  website .  The presentation will be at noon on Wednesday, March 7 in Room N354, CGIS North, 1737 Cambridge St. As always, lunch will be provided. An abstract of the paper follows on the jump: 
 


 UNIFORM INFERENCE IN AUTOREGRESSIVE MODELS 
Anna Mikusheva 

 Abstract 

 The purpose of this paper is to provide theoretical justification for some existing methods 
of constructing confidence intervals for the sum of coefficients in autoregressive models. 
We show that the methods of Stock (1991), Andrews (1993), and Hansen (1999) provide 
asymptotically valid confidence intervals, whereas the subsampling method of Romano and 
Wolf (2001) does not. In addition, we generalize the three valid methods to a larger class 
of statistics. We also clarify the difference between uniform and point-wise asymptotic 
approximations, and show that a point-wise convergence of coverage probabilities for all 
values of the parameter does not guarantee the validity of the confidence set.